A Meta-Learning Approach Towards Microvessel Classification Based on PAC-Bayes

摘要

In this work, we proposed a meta-learning method for the classification of microvessel images based on PAC-Bayes.We first introduce the modeling of Single-Opponent (SO) neurons to capture the color information of microvesselimages. Then, we presented the PAC-Bayes bound on multiple learning tasks for the classification of microvessel imagesby optimizing the PAC-Bayes objective function. Further, we summarize the meta-learning algorithm based on PACBayesto classify microvessel images in detail. The proposed method is superior in precision and f1-score compared withother representative methods.